Arrhythmia discrimination Using a smart phone

Jo Woon Chong, Nada Esa, David D. McManus, Ki H. Chon

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


We hypothesize that our smartphone-based arrhythmia discrimination algorithm with data acquisition approach reliably differentiates between normal sinus rhythm (NSR), atrial fibrillation (AF), premature ventricular contractions (PVCs) and premature atrial contraction (PACs) in a diverse group of patients having these common arrhythmias.We combine root mean square of successive RR differences and Shannon entropy with Poincare plot (or turning point ratio method) and pulse rise and fall times to increase the sensitivity of AF discrimination and add new capabilities of PVC and PAC identification. To investigate the capability of the smartphone-based algorithm for arrhythmia discrimination, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion, as well as seven participants with PACs and four with PVCs were recruited. Using a smartphone, we collected 2-min pulsatile time series from each recruited subject. This clinical application results show that the proposed method detects NSR with specificity of 0.9886, and discriminates PVCs and PACs from AF with sensitivities of 0.9684 and 0.9783, respectively.

Original languageEnglish
Article number7073588
Pages (from-to)815-824
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number3
StatePublished - May 1 2015


  • Arrhythmia
  • Atrial fibrillation
  • Poincare plot
  • Premature atrial contraction
  • Premature ventricular contraction
  • Root mean square of successive RR differences (RMSSD)
  • Shannon entropy
  • Turning point ratio


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